Costs and Benefits 

of Conservation 

Voltage Reduction

CVR Warrants Careful Examination 

INITIAL FINDINGS | NOVEMBER 15 2013

NRECA-DOE SMART GRID DEMONSTRATION PROJECT | DE-OE0000222
Prepared by

David Pinney

Cooperative Research Network

National Rural Electric Cooperative Association

4301 Wilson Boulevard

Arlington, Virginia 22203-1860

for

U.S. DOE/NETL

Morgantown Campus

3610 Collins Ferry Road

PO Box 880

Morgantown WV 26507-0880

NRECA-DOE SMART GRID DEMONSTRATION PROJECT | DE-OE0000222

project manager: craig miller, phd principal investigator: tom lovas
The National Rural Electric Cooperative Association

NRECA is the national service organization for more than 900 not-for-profit rural electric cooperatives

and public power districts providing retail electric service to more than 42 million consumers in 47 states

and whose retail sales account for approximately 12 percent of total electricity sales in the United States.

NRECA’s members include consumer-owned local distribution systems — the vast majority — and

66 generation and transmission (G&T) cooperatives that supply wholesale power to their distribution

cooperative owner-members. Distribution and G&T cooperatives share an obligation to serve their

members by providing safe, reliable and affordable electric service.

About CRN

NRECA’s Cooperative Research NetworkTM (CRN) manages an extensive network of organizations and

partners in order to conduct collaborative research for electric cooperatives. CRN is a catalyst for innovative

and practical technology solutions for emerging industry issues by leading and facilitating collaborative

research with co-ops, industry, universities, labs, and federal agencies.

CRN fosters and communicates technical advances and business improvements to help electric cooperatives

control costs, increase productivity, and enhance service to their consumer-members. CRN products,

services and technology surveillance address strategic issues in the areas:

• Cyber Security

• Consumer Energy Solutions

• Generation & Environment

• Grid Analytics

CRN research is directed by member advisors drawn from the more than 900 private, not-for-profit,

consumer-owned cooperatives who are members of NRECA.

Contact Information

National Rural Electric Cooperative Association

Cooperative Research Network TM

4301 Wilson Boulevard

Arlington, VA 22203-1860

703.907.5500

• Next Generation Networks

• Renewables

• Resiliency

• Smart Grid
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Table of Contents

Page

Foreword....................................................................................................................................... iii

Acknowledgments........................................................................................................................ iv

Abstract...........................................................................................................................................1

Executive Summary of Results.....................................................................................................1

Research Questions........................................................................................................................1

Technology Description.................................................................................................................2

Field Deployments..........................................................................................................................3

Adams-Columbia Electric Cooperative .....................................................................................3

Motivation.........................................................................................................................3

Installation Description.....................................................................................................3

Planning Experience .........................................................................................................4

Deployment Status............................................................................................................4

Deployment Lessons Learned...........................................................................................4

Realized Benefits..............................................................................................................4

Owen Electric Cooperative ........................................................................................................4

Motivation.........................................................................................................................4

Installation Description.....................................................................................................4

Deployment Status............................................................................................................5

Iowa Lakes Electric Cooperative ...............................................................................................5

Motivation.........................................................................................................................5

Installation Description.....................................................................................................5

Planning Experience .........................................................................................................6

Deployment Status............................................................................................................6

Verification and Realized Benefits...................................................................................6

Verification .....................................................................................................................................7

Modeling CVR Power System Behavior ......................................................................................8

Static Peak and Mean Powerflow Method.................................................................................9

Dynamic Powerflow Method...................................................................................................11

Accuracy of Dynamic Powerflow Models...............................................................................12

Costs and Benefits........................................................................................................................15

References.....................................................................................................................................16

Appendix A: Verification Code ............................................................................................... A-1
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List of Figures

Page

Figure 1. Gar Substation Voltage Reduction................................................................................. 7

Figure 2. Verification Procedure Dataflow.................................................................................... 8

Figure 3. Specifying Capacitor Bank Properties in the OMF...................................................... 10

Figure 4. Dynamic Powerflow Simulation—Control of Capacitor Bank Switching .................. 12

Figure 5. Three-Month Dynamic Powerflow Simulation—Powerflow across 

the Substation, and Loads and Losses Breakdown...................................................... 12

Figure 6. Comparison of Physical Water Heater Model versus Zip Model at Three Voltage 

Levels ........................................................................................................................... 13

Figure 7. Peak Reduction Event-Shifting Voltage from 124 to 116 and Comparing Physical 

Water Heater Load vs. ZIP Load.................................................................................. 14

Figure 8. Utility Savings by Month and Cause............................................................................ 16

List of Tables

Page

Table 1. Powerflow Results for Test Co-Op Feeder......................................................................11

Table 2. Comparison of Energy Consumption and Peak Demand ................................................14

Table 3. Costs and Benefits for Re-Regulation of Test Feeder .....................................................15
NRECA – DOE Smart Grid Demonstration Project | DE-OE0000222

–iii–

FOREWORD

The National Rural Electric Cooperative Association (NRECA) has organized the NRECA-U.S. 

Department of Energy (DOE) Smart Grid Demonstration Project (DE-OE0000222) to install and 

study a broad range of advanced Smart Grid technologies in a demonstration that involves 23 

electric cooperatives in 11 states. For purposes of evaluation, the technologies deployed have 

been classified into three major sub-classes, each consisting of four technology types, the status 

of which have been reported in the Interim Technology Report of April 2013:

Enabling Technologies: Advanced Metering Infrastructure

Meter Data Management Systems

Telecommunications

Supervisory Control and Data Acquisition

Demand Response: In-Home Displays & Web Portals

Demand Response Over AMI

Prepaid Metering

Interactive Thermal Storage

Distribution Automation: Renewables Integration

Smart Feeder Switching

Advanced Volt/VAR Control

Conservation Voltage Reduction

To demonstrate the value of implementing the Smart Grid, NRECA has prepared a series of 

single-topic studies to evaluate the merits of project activities. The study designs have been 

developed jointly by NRECA and DOE. This document is the initial report on one of those 

topics, based upon the progress of the activity to date. The project team will be monitoring the 

progress of the various cooperative activities during the remaining term of the demonstration to 

close identified information gaps and identify additional information that will be of benefit to the 

merit evaluation. This document and the other single-topic studies then will be updated, as 

appropriate, for consideration in the final Technology Performance Report at the close of the 

Smart Grid Demonstration Project.

DISCLAIMER

The views as expressed in this publication do not necessarily reflect the views of the U.S. 

Department of Energy or the United States Government.
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ACKNOWLEDGMENTS

We are grateful for the input from the following, without which this report would not have been 

possible:

 James Bridges, VP of Engineering, Owen Electric Cooperative

 Robert Emgarten, Manager of Engineering, Iowa Lakes Electric Cooperative

 Jason Fuller, Senior Research Engineer, Pacific Northwest National Laboratory

 Gerald Schmitz, Electrical Engineer, Adams-Columbia Electric Cooperative
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ABSTRACT

This report investigates the deployment experience at four rural electrical cooperative utilities of 

conservation voltage reduction (CVR) technology. Data from these field studies are used in the 

development and calibration of a hybrid powerflow-economic model. We derive a cost-benefit 

analysis methodology for conservation voltage reduction from this model and validate it against 

field data.

EXECUTIVE SUMMARY OF RESULTS

Volt/VAR optimization (VVO) via power factor correction was preferred strongly to CVR via 

active voltage regulation. CVR schemes were primarily SCADA actuated but were initiated by 

human operators. Simple paybacks for these projects were generally in the 0–2 year range.

Model data are not always detailed enough for time series powerflow. Model results were not 

informative if the underlying data that inform the model were lacking. Heuristics based on 

historical CVR factors, single point-in-time voltage drop, and annual energy use estimates can be 

used for estimates of CVR effectiveness. Calibration of dynamic load models is still a manual 

and labor-intensive process.

Many schemes are in common use for verifying CVR results. A central problem is how we pair 

control and CVR-influenced data. Use of correlated feeders is a faster and clearer method than 

alternate day comparison with weather correction.

RESEARCH QUESTIONS

 Field Trials:

 In the co-ops that installed hardware, what were the expected and realized benefits?

 What are the planning requirements for a successful CVR deployment?

 What are best practices and common “gotchas” across all deployments?

 What is the best method for verifying results?

 How do energy savings and demand savings impact revenue for the co-op and its 

members?

 What feeder characteristics are correlated with benefits?

 Model Extensions:

 How can the CVR algorithm be tuned to a tradeoff between costs and benefits?

 How much engineering design (e.g., capacitor sizing and siting) can be automated?

 Are model results comparable to human-led planning studies?
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TECHNOLOGY DESCRIPTION

The key principle of CVR operation is that the ANSI standard voltage band between 114 and 126 

volts can be compressed via regulation to the lower half (114–120) instead of the upper half 

(120–126), producing considerable energy savings at low cost and without harm to consumer 

appliances. Decades of field research have found that for each 1% reduction in distribution 

service voltage, mean energy consumption for residential and commercial loads is reduced by 

0.8%. Furthermore, these energy savings can be highly economical to capture. Variation in 

results depends on load mix and distribution system configuration.

As an illustrative example, one of the earliest CVR pilot projects was done in 1987 by 

Snohomish PUD, which concluded that, across three test substations, a levelized 2.1% voltage 

reduction was achievable, as well as reduced energy requirements by approximately the same 

amount. System loss effects of voltage reduction were favorable, with the bulk of the reduction 

resulting from an improvement in distribution transformer efficiency. Customer bills, after a rate 

adjustment to accommodate fixed operations costs, were approximately $6.28 lower per 

customer per year. These savings were available at a cost of $0.008/kWh for additional line drop 

compensator and capacitor application.

“CVR factor” is the term commonly used to refer to the ratio between voltage reduction and 

energy load consumption for a particular part of a distribution system (load, feeder, substation, or 

utility):

Factors vary widely from substation to substation, feeder to feeder, and especially load to load. 

Contributions to the overall factor for a utility include consumers' load mix, transformer and 

conductor characteristics, voltage control schemes as moderated by voltage regulators, line drop 

compensators, and switched capacitor banks. Because of the large number of components 

involved, CVR factors for feeders and substations typically are measured experimentally, not 

theoretically generated. An excellent overview of measured feeder CVR factors is included in 

DSTAR's evaluation of CVR [18].

Progress is being made in calculating CVR factors theoretically, with an eye toward predicting 

control scheme performance before installation. In this report, we investigate heuristic and load 

model-based approaches.

Load behavior is a large contributor to feeder CVR factor. Many load modeling studies have 

been completed; a good recent study is the 2010 report by the Pacific Northwest National 

Laboratory (PNNL), which evaluated CVR on a national level and built models that divided 

loads into two primary classes: those with and without thermal cycles. In the first category, 

lighting loads, for example, will consume energy as a function of voltage when on. In the second 

category, loads with thermal cycles, such as a hot water heater, will vary their duty cycles 

depending on the supply voltage. Moreover, inside each of these classes, loads' response can be 

described by their ratio of constant power, impedance and current characteristics—ZIP models. 

ZIP models can be constructed from experimental results on load behavior under changing 

voltage conditions.

Key goals of conservation voltage reduction are peak power demand reduction and energy 

conservation. These benefits are available at different prices, depending on the distribution 

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system; in general, however, CVR is seen as very cost-effective. In a planning study performed 

by the Bonneville Power Administration (BPA) across 150 utilities in its service area, BPA 

found 170 to 268 MWh of energy conservation opportunity (and hence generation capacity 

increase deferment) priced at between 0.01 and 5 cents/kWh [3].

Feeder characteristics that correlate with CVR are of interest for planning purposes and are 

addressed later in this report. BPA's findings in the study cited above were that short feeders with 

large numbers of customers were the most economical to use when applying CVR techniques; 

however, later studies have challenged this result [5].

Hardware choices for improved volt/VAR optimization boil down to capacitor banks, voltage 

regulators, and improved measurement and control systems. Other upgrades to the distribution 

system that improve the voltage profile—such as reconductoring, load balancing, and 

transformer upgrades—also can be helpful, in combination with the more technically 

sophisticated approaches mentioned above. Existing research on the effectiveness of these 

various methods has singled out the cost effectiveness of improved VAR support and better 

voltage regulation schemes; reconductoring and adding completely new regulators is more 

expensive.

FIELD DEPLOYMENTS

Four cooperatives completed volt/VAR optimization projects. Descriptions of the deployments

follow.

Adams-Columbia Electric Cooperative

Motivation

Adams-Columbia Electric Cooperative (ACEC) is a utility serving 36,000 members around 

Friendship, Wisconsin. The cooperative's main goal in implementing CVR was to reduce 

monthly coincident peak demand charges from its power supplier, Alliant Energy. ACEC also 

sought to improve system power factor and voltage profile.

Installation Description

ACEC installed 30 voltage monitoring sites, along with 10 distribution regulators. The CVR 

activity included installation of 10 capacitor banks with controller and 40 Varentec solid state 

variable capacitors. Capacitor banks allow for a flattening of the system voltage profile, 

improving the abilities of substation regulators to perform conservation voltage reduction. The 

CVR control algorithm currently is triggered manually by employees when they deem the 

system's peak will coincide with a power supplier peak. A regulation activity, when triggered, 

reduces substation voltage for 4–5 hours and then restores the original voltage level.

The Varentec devices are transformer-mounted edge-of-network devices, described by the 

vendor as "voltage optimizers." The devices each include 10 kVAR switched capacitor banks, 

monitoring sensors, and cellular modems. Although quite expensive per kVAR when compared 

to traditional switched capacitor banks, the devices offer more advanced controls, the possibility 

of more precise sizing and location, and monitoring functionality. This hardware deployment 

also was motivated by the cooperative's desire to study and pilot a unique and cutting-edge 

technology.

Total hardware and software costs for this project were $176,000.
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Planning Experience

ACEC contracted with a third-party engineering firm, Power Systems Engineering (PSE), to 

perform a planning study to determine the suitability of each of its feeders for CVR. Each feeder 

was modeled using Milsoft's Windmil engineering analysis software, and recommendations of 

estimated potential one-year savings for each feeder were generated. Approximately half of the 

studied feeders were found to have 0- to 2-year estimated simple paybacks for project hardware, 

based on lower peak demand charges.

Deployment Status

ACEC’s Varentec hardware is fully installed and operational, but data collection is on hold 

pending a firmware upgrade to fix a communications issue, in which the devices would not join 

the correct cellular data network. ACEC so far has seen a 10% failure rate for these devices. 

The substation regulators for the more traditional CVR implementation have been installed, and 

the radio for end-of-line voltage measurement was deployed, but the supervisory control and data 

acquisition (SCADA) control algorithm had not been implemented as of September 17, 2013. 

The capacitor controls also need some additional work.

Deployment Lessons Learned

ACEC was concerned that cap banks would block AMI signals on its power line carrier system 

by sending signals to ground. To avoid this problem, the cooperative used line-to-ground 

capacitors, with blockers installed on the neutral phase, to maintain signal integrity. No signal 

degradation has been found so far.

Installation problems comprised typical administrative, legal, and construction issues, and were 

not specific to the smart grid technology being installed. Weather head additions to coax cable 

termination resulted in damage to low-density foam (LDF) cables, which needed replacement. 

For a communications upgrade, ACEC proposed building a 70-foot steel pole on land owned by 

another utility. Obtaining easement proved to be a multi-month process; to stay on schedule, the 

tower instead was deployed on a farmer's land, resulting in additional site engineering.

Realized Benefits

ACEC did a session-initiation protocol (SIP) programming test on a regulator substation, 

lowering voltage and achieving a resultant load reduction. However, this is too small a sample to 

come to any conclusions at this point. Full verification methodology and data are described later 

in this study.

Owen Electric Cooperative

Motivation

Owen Electric Cooperative (OEC) is a utility serving approximately 58,000 consumer-members 

around Owenton, Kentucky. The purpose of the project is to gain enhanced knowledge of the 

effects of optimizing OEC's system voltage and kVAR profiles with respect to peak electrical 

demand and energy usage.

Installation Description

Substations that serve Owen Electric Cooperative are configured for bus regulation. OEC 

currently requires its power provider, Eastern Kentucky Power Cooperative (EKPC), to set the 

bus voltage regulators to 125 volts +/- 1 volt (referenced to 120 volts). Line voltage regulators 

(VRs) are used on the OEC distribution system to support the system voltage. VRs typically are 
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set to 125 volts +/- 1 volt (referenced to 120 volts) and can raise and lower line voltage levels up 

to 10%. (The 125-volt setting can allow up to an 8-volt drop on the system past the VR.) 

Voltage-level adjustments of less than a volt are typical of VRs. Auto-booster transformers 

(ABs) also are used for voltage support when tight voltage bandwidths are not necessary. Autobooster transformers have less capability than line voltage regulators to react to and compensate 

for voltage fluctuations. ABs typically are set to 125 volts, with a 4-volt bandwidth. Typical 

voltage-level adjustments are 1.5 volts. Since ABs are limited in their ability to maintain tight 

voltage bandwidths, they were not recommended for this project.

EKPC currently requires its member cooperatives to maintain a power factor of 90% (lagging) or 

better at the distribution station transformer level. EKPC assesses financial penalties monthly 

when the power factor falls below this level. EKPC does not assess penalties for leading power 

factor. To maintain power factor levels at or above 90% lagging, OEC historically has utilized 

fixed capacitor banks. These capacitor banks have been furnished by EKPC to its member 

cooperatives as an incentive to maintain compliant power factor. Typical fixed capacitor banks 

are sized at 300 and 600 kVAR.

To improve voltage regulation and power factor, OEC's Advanced volt/VAR Control activity is 

being implemented in phases at two substations:

Phase 1: Verify and correct system data so that the engineering model is accurate in all 

critical areas.

Phase 2: Analyze and optimize feeders for phase balancing and power factor.

Phase 3: If voltage optimization is possible, test and evaluate the effects of reducing voltages 

on the feeders.

Phase 4: If cost beneficial, deploy an Integrated Volt/VAR Control (IVVC) system at one or 

both test substations.

Phase 5: Conduct data collection and verification.

Deployment Status

OEC is in Phase 3 of its four-phase project. This measurement phase will indicate whether 

regulation changes are possible and if they are valuable to consumer-members. OEC is in the 

process of changing its system design to use alternate voltage monitors. The original equipment 

had functional problems and was returned to the manufacturer.

Iowa Lakes Electric Cooperative

Motivation

Iowa Lakes Electric Cooperative (ILEC) is a utility serving 12,289 customers around Estherville, 

Iowa. ILEC's primary goals for conservation voltage reduction were to reduce demand charges 

from power suppliers and improve power factor.

Installation Description

Four substations (Gar, Range, Miles Nelsen, and Milford) were set up for 2.5% or 3-volt 

reduction at monthly coincident peak times. The three voltage regulator panels at each substation 

are controlled and monitored through the SCADA system. The 2011 summer demands on these 

substations were 5,900 KW at Gar sub; 2,228 KW at Milford sub; 1,290 KW at Range sub; and 

2,208 KW at Miles Nelsen sub. At the project planning stage, it was projected that a 2.5% drop 

in voltage would yield a 2% drop in the current KW demand on the substation, based on 
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historical results of comparable CVR installations. The planning was restricted to residential 

substations.

KVAR capacitor bank controls also were deployed in two substations (Rembrandt and Gilmore 

City) that historically had power factor averaging around 70%. These were improved with a 150-

kVAR bank at Gilmore City substation and a 1,200-kVAR bank at Rembrandt substation. These 

kVAR capacitor banks reduced the overall KVA demand on each substation transformer and 

improved the voltage levels, reaching a target power factor of 85%. Each of these units has a 

controller that can energize the bank either by time or voltage levels, and current operation is 

based on a time-of-day schedule.

Two municipal utilities (Pocahontas and Estherville) are a part of ILEC’s sale-for-resale 

accounts, and each wanted to reduce its monthly coincident demand. Each needed to be able to 

monitor its demand as it compares to the wholesale power supplier and enable the control of its 

monthly demand at coincident billing peaks. The project included communication equipment at 

each municipal substation to allow real-time load demand monitoring at city hall and also 

required communication at each substation to enable control of load management devices. Due to 

the municipal utilities’ lack of approval to go ahead with the project, this effort was cancelled.

Planning Experience

Planning at this cooperative was conducted by Bob Emgarten. Prior experience in deploying 

CVR controls at four Minnesota cooperatives led Mr. Emgarten to investigate CVR as a demand 

management technique for ILEC. Design parameters and hardware choices from this experience 

informed the demonstration project planning, including the deployment of CVR controls at main 

residential substations, with monitoring and control via SCADA.

Existing substation voltage regulator panels had active voltage control capabilities, which led to 

significant cost savings over turnkey CVR solutions. The main substation expense came from 

modifying the SCADA display to include control signals for the voltage regulators.

Deployment Status

The hardware is deployed and functioning correctly in the field. CVR operation was started 

under SCADA control on January 1, 2012.

Verification and Realized Benefits

ILEC does not have a requirement to keep a historical database of SCADA readings. Because of 

this constraint, the verification procedures described in the Verification section of this report 

were not applied to this project.

The original CVR plan called for a 5% instead of a 2.5% voltage reduction. ILEC has 

implemented a smaller voltage reduction thus far to guard against power quality issues. Further 

voltage reduction can be implemented in the SCADA system if there are no problems with the 

current program.

Voltage reduction has been verified via a SCADA display upgrade, and a matching load 

reduction is observable. In Figure 1, Gar substation voltage reduction, note the voltage and load 

reductions. The top graph shows voltage level, with scheduled reduction at 2 p.m. The bottom 

graph shows KW demand. The load spikes are from a demand response program operated by the 

power producer, not a product of the CVR regulation.
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Figure 1. Gar Substation Voltage Reduction

VERIFICATION

Verification of the benefits of volt/VAR optimization is a difficult problem. Load changes occur 

for many reasons and are hard to separate from the changes to the powerflow due to volt/VAR 

optimization. There are two verification approaches commonly described in the literature: 

comparison of strongly correlated feeders (correlated feeder method) and comparison of one 

feeder across time with weather correction (alternate day method). The alternate day method 

typically is practiced by applying the CVR scheme on alternate days to factor out seasonal load 

changes [5].

We base our verification scheme on testing across correlated feeders. There are some key 

advantages to this approach. The pairing algorithm is uniquely defined and simple to implement. 

Weather and day-of-week load correction is not necessary because the pairs of SCADA 

measurements under comparison are taken at the same time. This method also has operational 

benefits, since feeder regulation schemes do not need to be changed frequently, as is the case in 

the single-feeder verification methods cited in the literature. With ACEC, we found that, for each 

of the features for which CVR was implemented, there were multiple other feeders in the system 

whose load behavior was strongly correlated (R^2 > 0.9), which we could use as controls. 

In cases for which, due to the system design, highly correlated feeders do not exist, we propose 

an alternate day treatment verification protocol.

The full source code for our verification scheme is available in Appendix A, along with 

interspersed example data from ACEC. As of November 15, 2013, the CVR installations were all 

complete, but data collection had just begun. To accurately evaluate CVR, a year’s worth of 
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SCADA data is required to capture the effects of seasonal Heating, Ventilation, and Air 

Conditioning (HVAC) loads. We foresee collecting and running these data through our analysis 

code in late 2014.

A dataflow diagram for the verification procedure is shown in Figure 2. From a set of SCADA 

data for the target feeder substations (typically provided as tab-separated value flat files), we 

derive a standard form (meter ID, timestamp, power, voltage, power factor); produce a 

correlation matrix for each pair of feeders based on a subset of the data as a control; select the 

most strongly correlated feeder for each treated CVR feeder; and then measure the relevant 

quantities (∆EE, ∆Demand, FFCVR ) from these pairs.

Figure 2. Verification Procedure Dataflow

Future work to compare the sensitivity and specificity of the correlated feeder method with that 

of the alternate day method would be beneficial in determining the ideal verification protocol. To 

do so via a dynamic powerflow model would be straightforward. To test in the field would 

require disaggregated, high-resolution (hourly) load data from AMI. For the systems studied in 

this report, load data at this resolution were not available, due to communications network 

limitations (ILEC) or because meter data management software was not yet available (ACEC).

We also foresee AMI data as having some importance in CVR verification, although clearly this 

is not a requirement, as proven by these study cooperatives’ success and the history of the 

technology. There is some demand for AMI data in exception reporting for meters out of the 

ANSI voltage limits as an indicator of power quality issues.

MODELING CVR POWER SYSTEM BEHAVIOR

In this section, we derive computational model-based results for the CVR behavior on the 

studied systems. We model at three distinct levels of load detail and compare the results. Models 

are built at the level of the feeder due to the feeder-by-feeder planning and investment decision 

process used by the study participants and volt/VAR optimization projects in general.

The key problem that any computational model of CVR must solve is powerflow. Given a 

description of the hardware on the feeders (lines, transformers, capacitor banks, etc.) and a 

description of the loads on the system, we must solve for the total power consumption across all 

of the loads and power dissipated in the distribution system (losses). Changing the operating 

conditions (source voltage) to reflect the behavior of the CVR hardware allows us to judge those 

systems’ efficacy. Aggregating the powerflow results over time gives us total energy delivered 

and lost and peak demand, which are the key variables of interest for economic analysis.
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We rely on the National Rural Electric Cooperative Association’s (NRECA’s) Open Modeling 

Framework (OMF, accessible at http://omf.coop/) to perform our analysis. The OMF is an open 

source framework for performing analysis with models of the electrical grid. Faced with 

numerous models created by the research community, vendors, and utilities, the OMF provides a 

structure for running, comparing, reporting on, and monetizing the results. This is managed via a 

web interface, enabling collaboration and sharing of the results.

The OMF incorporates GridLAB-D, a state-of-the-art feeder simulator developed by PNNL and 

released to the public as open source software. The OMF relies on GridLAB-D to perform 

powerflow calculations and as a means to describe controls schemes and load-weather 

interactions.

At its core, GridLAB-D has an advanced algorithm to simultaneously determine the state of 

millions of independent devices, each described by models and equations relevant to the 

particular domain. GridLAB-D does not require the use of reduced-order models to describe the 

aggregate behavior of the system (but may do so when appropriate). Rather, it relies on advanced 

physical models to describe the interdependencies of each of the devices. This helps to avert the 

danger of erroneous or misapplied assumptions. The advantages of this algorithm over 

traditional, finite difference-based simulators are that (1) it handles unusual situations much 

more accurately; (2) it handles widely disparate time scales, ranging from sub-seconds to many 

years; and (3) it is very easy to integrate with new models and third-party systems. This unique 

approach to power-system modeling has enabled industry, utilities, and others to use the tool to 

evaluate new distribution-automation designs (e.g., VVO, feeder reconfiguration, fault-detection 

identification and restoration); new rate structures in concert with new smart technologies (e.g., 

real-time pricing and automated controls, direct load control); optimization of distributed-energy 

resource usage (e.g., maximizing the value of battery storage for peak-load shaving, arbitrage, 

and regulation); benefits and effects of new technologies (e.g., voltage control issues with high 

penetration of photovoltaics); and a number of other studies designed to maximize the potential 

of new technologies.

All of the system models in this study were translated into OMF standard format from Milsoft 

Utility Solutions’ Windmil software system. Among the 23 co-ops that participated in NRECA’s 

Smart Grid Demonstration Project, 80% had Windmil models of their entire system. Milsoft 

reports an 80%–90% market share among electric distribution cooperatives.

Static Peak and Mean Powerflow Method

Of the three methods we consider, the one that is computationally simplest, fastest to evaluate, 

and the current standard for planning studies in the cooperatives reviewed is a static monthly 

peak and mean powerflow calculation. 

As described above, we bring study feeder distribution hardware descriptions into the OMF from 

Windmil. Each base feeder then is duplicated, and the duplicate is modified using the OMF GUI 

to include any capacitor banks, line drop compensators, or other circuit elements installed as part 

of the project that would impact system losses or loads. An example of this editing process is 

shown in Figure 3.
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Figure 3. Specifying Capacitor Bank Properties in the OMF 

Load data also are imported from Windmil. For each meter on the system, real and imaginary 

load as a percentage of historical annual consumption is allocated based on billing data via 

Milsoft’s load allocation algorithm. In the OMF, these loads are translated into ZIP models—

load circuit elements that provide or consume a combination of constant impedance (Z), current 

(I), and power (P). Because the feeders under study were primarily residential feeders, we 

modeled loads as 50% constant impedance and 50% constant power, in line with industry 

practice and published examples [19].

A year's worth of historical SCADA data for the test feeder is used to determine peak and mean 

load levels for each month. The ZIP models then are scaled to 10 different load levels that can be 

used to linearly approximate all of the historical levels. Powerflow is run twice at each level: 

once at the current substation voltage and once at a substation voltage that is as low as possible 

with no meters outside of the ANSI voltage band (116–124 volts). The difference in 

consumption between the two voltage levels then gives the maximum possible savings that could 

be achieved via CVR. Model output for one example feeder is shown in Table 1.
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Table 1. Powerflow Results for Test Co-Op Feeder

Load Level 

(W)

CVR 

Opp.

CVR 

Factor

CVR 

Active

High 

Meter (V)

Low 

Meter (V)

Sub 

(V)

Losses 

(W) PF

Power 

Cons

1.0E+06 5.91E+04 1.04 FALSE 125 122 124 2.4E+04 96% 9.72E+05

TRUE 118 115 116 2.3E+04 97% 9.13E+05

1.6E+06 6.29E+04 1.05 FALSE 124 120 124 5.2E+04 100% 1.55E+06

TRUE 120 115 119 5.2E+04 100% 1.48E+06

2.2E+06 4.32E+04 1.05 FALSE 124 118 124 9.6E+04 99% 2.11E+06

TRUE 122 115 121 9.6E+04 99% 2.07E+06

2.8E+06 0.00E+00 0 FALSE 124 115 124 1.5E+05 98% 2.68E+06

2.68E+06 TRUE 124 115 124 1.5E+05 98% 2.68E+06

3.4E+06 0.00E+00 0 FALSE 123 113 124 2.3E+05 97% 3.24E+06

TRUE 123 113 124 2.3E+05 97% 3.24E+06

4.0E+06 0.00E+00 0 FALSE 123 111 124 3.2E+05 97% 3.80E+06

TRUE 123 111 124 3.2E+05 97% 3.80E+06

4.6E+06 0.00E+00 0 FALSE 123 108 124 4.2E+05 96% 4.35E+06

TRUE 123 108 124 4.2E+05 96% 4.35E+06

5.2E+06 0.00E+00 0 FALSE 123 106 124 5.4E+05 95% 4.90E+06

TRUE 123 106 124 5.4E+05 95% 4.90E+06

5.8E+06 0.00E+00 0 FALSE 123 103 124 6.8E+05 94% 5.44E+06

TRUE 123 103 124 6.8E+05 94% 5.44E+06

6.4E+06 0.00E+00 0 FALSE 123 101 124 8.4E+05 94% 5.98E+06

TRUE 123 101 124 8.4E+05 94% 5.98E+06

For this feeder, we see that there are CVR possibilities for load levels at and below 2.2 MW. 

This then can be translated into expected savings, as we will address in the Costs and Benefits 

section.

The full code to perform this analysis is available on request. Running time is approximately one 

minute on a modern workstation.

Dynamic Powerflow Method

To provide a model that better captures the time-dependent CVR effects on load, we built a highresolution dynamic time series model. This model is still in the process of verification and is 

included to indicate opportunities for future research.

In this dynamic model, feeder data come from Windmil, as before. Instead of static ZIP loads, 

we rely on GridLAB-D's ability to describe time-varying ZIP plug loads, along with HVAC, and 

its thermal interactions with weather and building architecture. (A full description of GridLABD's load modeling approach is beyond the scope of this report.) For sizing the loads, we replace 

the static load allocations from Windmil with house models, drawn randomly from a 

representative sample of residential houses and loads compiled by PNNL [2], then scaled 

according to the allocated load. 

As in the static model, we compare baseline powerflow to a powerflow scenario in which CVR 

is active. We use GridLAB-D's CVR control scheme, which has been published in the IEEE 

Transactions on Power Systems and is openly available [12]. This scheme has two major goals: 

voltage optimization and reactive power control. To achieve reactive power control, shunt 

capacitors on the distribution feeder are operated to maximize the power factor at the substation. 

Voltage optimization is achieved through operation of the substation voltage regulator to 

minimize system voltage while keeping the measured End-of-Line (EOL) voltage within the 
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ANSI band We do not consider control of additional downstream voltage regulators. (See 

Figures 4 and 5.)

Figure 4. Dynamic Powerflow Simulation—Control of Capacitor Bank Switching

Figure 5. Three-Month Dynamic Powerflow Simulation—Powerflow across the Substation, 

and Loads and Losses Breakdown

Accuracy of Dynamic Powerflow Models

ZIP models are commonly used for modeling power system loads and estimating the benefits of 

VVO mechanisms. ZIP models provide excellent simulation results when looking at a single 

snapshot in time or the instantaneous power reduction provided. However, when studying the 

longer-term dynamics of voltage reduction, ZIP models are incapable of capturing the more 

complex behavior of loads, especially those driven by closed-loop control (e.g., thermostatic 

controls, such as HVAC or water heaters). Studying these effects requires more detailed load 

models that represent detailed behavior, particularly how voltage affects energy consumption and 

power demand in the presence of a control loop. 
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A single water heater provides a good example of a thermostatically controlled load that is 

poorly represented by a ZIP model. A water heater is modeled as a purely resistive element 

(100% impedance) in a ZIP model. GridLAB-D represents the behavior of the water heater as a 

physical process that determines the flow of heat energy; this is known as a physics-based or 

physical load model. The amount of heat energy within the water, or the temperature of the 

water, is affected by the amount of insulation around the water heater (also called the thermal 

jacket) and hot water usage in the home. GridLAB-D models the current temperature of the 

water, compares it to the thermostat set points, and determines when the device should be on or 

off. When the device is on, a voltage-dependent resistive element is applied to heat the water; 

i.e., if the voltage is lower, less power is demanded and therefore less heat is produced. This 

requires the device to run for a longer amount of time, as the same amount of energy is needed to 

heat the water to the desired temperature. In a ZIP model, a reduction of voltage results in 

reduced power demand and reduced energy consumption. However, in a physical model, a 

reduction in voltage results in a lower power demand but a longer run time for the device. Figure 

6 highlights this issue. Notice that the ZIP load (right-hand figure) shows the run time as 

constant at different voltage levels, reducing both energy and power, while the physical load 

model (left-hand figure) extends the run time at lower voltages, reducing power but not energy.

Figure 6. Comparison of Physical Water Heater Model versus Zip Model at Three Voltage Levels

We also can look at a collection of water heaters for two different volt/VAR services: peak 

reduction and energy reduction. Below is an example from GridLAB-D that simulates 1,000 

water heaters as both physical and ZIP models. Figure 7 shows a 2-hour peak reduction by 

lowering the voltage from 124 to 116 from 15:00 to 17:00; while this represents an extreme case, 

it highlights the effects. The dashed lines represent the ZIP model, while the solid line represents 

a physical model of the water heater (WH). At 15:00, both the ZIP and WH load are reduced by 

about 15%. However, by 16:00, the physical model reduction has significantly decreased as the 

water heaters return to their natural operational state (consuming the same amount of energy), 

while the ZIP model still shows a reduction of approximately 15%. In this 2-hour window, the 

ZIP model predicts that peak reduction will be far greater than it actually is.
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Figure 7. Peak Reduction Event-Shifting Voltage from 124 to 116 and Comparing 

Physical Water Heater Load vs. ZIP Load

VVO also can be used for energy reduction, operating the system at all times at lower voltages. 

Again, 1,000 water heaters are simulated as both physical and ZIP models for one day, operating 

the system at 124, 120, and 116 volts constantly throughout the day. The results are shown in 

Table 2—daily energy consumption and peak demand during the evening peak per water heater. 

Note that the physical model shows only a minimal change in the energy consumption, while the 

ZIP load indicates a 14% reduction, from 124 V to 116 V. Also note that the ZIP model predicts 

a greater peak reduction (14% reduction, from 124 V to 116 V) than the physical model (4% 

reduction).

Table 2. Comparison of Energy Consumption and Peak Demand

Daily Energy Usage (kWh) Evening Peak (kW)

ZIP, 124 V 14.94 1.374

ZIP, 120 V 13.99 1.287

ZIP, 116 V 13.07 1.203

WH, 124 V 14.01 1.307

WH, 120 V 13.99 1.287

WH, 116 V 13.96 1.262

Traditional ZIP models are appropriate for single instances in time, i.e., instantaneous reduction 

of load when lowering the voltage, but are inadequate for capturing the time-series effects of 

voltage reduction. Physical models that capture the dynamic behavior of the loads, including 

thermostatic control loops, are required to understand how load is affected by a change in 

voltage, either for energy or peak reduction. While this case has used water heaters as an 

example, the issues are equally valid for HVAC, albeit with different reduction numbers.
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COSTS AND BENEFITS

Benefits of conservation voltage reduction accrue primarily to the utility and customers. We do 

not address the benefits of energy conservation to other groups as part of this study.

The CVR benefit with the largest and clearest payback, and hence of most interest to the 

cooperatives studied, was peak demand reduction. Loss reduction is another benefit. The 

principal cost of CVR programs is for hardware. Energy sales also are reduced as an effect of 

CVR.

Following the availability of data for validation, a summary of the realized costs and benefits of 

each project will be possible. At the time of writing, we considered expected costs and benefits 

from model results.

Static Load Model Cost-Benefit

In Table 3, we derive a cost-benefit analysis from the same model powerflows calculated in 

Table 1. For each historical month from a prior year's SCADA data, we use the historical peak 

and average loads to estimate peak/loss and energy consumption reductions, respectively. Each 

month's reduction watt values are the difference between the treated (CVR) model and the 

baseline, without re-regulation or capacitor bank additions. Results were interpolated linearly 

from nearest load matches among the 20 candidate powerflows to reduce the running time of the 

computations. A graph of these data is shown in Figure 8.

Table 3. Costs and Benefits for Re-Regulation of Test Feeder

Month Season

Historical Loads (kW) Peak Red. Energy Red. Loss Red. Net

Avg Peak kW $ kWh $ kW $ $

January Winter 2740 4236 0 0 19.45 -778 0.06 4 -774

February Winter 2483 3312 0 0 10.20 -408 0.03 2 -406

March Spring 2031 2964 0 0 22.59 -904 0.17 10 -893

April Spring 2107 3025 0 0 26.58 -1063 0.20 12 -1051

May Spring 2344 4076 0 0 5.19 -208 0.02 1 -206

June Summer 2769 5811 0 0 20.50 -820 0.07 4 -816

July Summer 3967 6746 0 0 0.00 0 0.00 0 0

August Summer 3274 5204 0 0 0.00 0 0.00 0 0

September Fall 2130 4904 0 0 27.78 -1111 0.21 13 -1099

October Fall 1752 2337 4.94 29613 7.97 -319 0.06 4 29297

November Fall 2208 3545 0 0 0.29 -12 0.00 0 -11

December Winter 2482 3365 0 0 10.16 -406 0.03 2 -404
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Figure 8. Utility Savings by Month and Cause

As Figure 8 makes clear, the main benefit for the re-regulation of this feeder is a lucrative peak 

reduction in October. Loss reductions offer trivial savings. Lower energy sales are a significant 

cost but are far outweighed by the demand reduction savings. In some markets, energy savings 

can be recovered through conservation credits. The model assumes that CVR is run continuously 

to keep the system voltage as low as possible while still keeping all meters within the ANSI 

band. Were the system to be run only during peaks, the lost energy sales would be drastically 

reduced.

For customers, CVR paradoxically tends to lower each customer bill while also raising energy 

rates. The result is a net saving for customers.

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APPENDIX A: VERIFICATION CODE

The Mathematica code for the algorithms described in the verification section follows. 

Comments are included, and output is in line with the code that produced it. The source is 

available on request.
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